A PID controller is a controller relating to a control loop having a feedback mechanism, and is a commonly used feedback controller in an industrial control system. PID controllers are employed in control loops to minimize error due to the difference between a measured process variable and a desired setpoint by adjusting the process control inputs.
PID control involves constant parameters corresponding to proportional (P), integral (I) and derivative (D) values that are interpreted in terms of time, in which P depends on the present error, I on the accumulation of past errors, and D being a prediction of future errors. The weighted sum of these three parameters P, I and D is utilized in adjusting the process through a control element, such as position of a control valve etc.
In practice, the controllers are tuned through adjustment of the control parameters in order to achieve a desired control response. Accordingly, PID controllers also involve tuning which is not trivial, even though there are only three control parameters involved; the tuning of the PID controller should satisfy complex criteria within the limitations of PID control such that it is a feedback system, the overall performance is reactive, etc.
The tuning of PID control based on heuristic rules, or any other rules or the like known until now, focuses on single loop performance and does not address process interactions. However, it can become important to consider and address process interactions to achieve better control of the processes.
A system is said to be multivariable if it has more than one variable to be controlled or more than one variable which can be manipulated. Multivariable PID caters to the above noted process interactions, and corresponds to multiple single loop PIDs with tuning that addresses the process interactions. Thus, Multivariable PID can be better as compared to single loop performance because it captures process information into the tuning rules. However, Supervisory control such as Model Predictive Control (MPC), Fuzzy control, etc can ensure better controller performance. MPC for example, accounts for constraints on the system while minimizing the output error in a predictive manner, accounting for the predictive capability of a process model into tuning rules.
Currently, PID controllers continue to be predominantly used controllers in process industries because of more practically known reasons such as legacy, reliability, simplicity etc. Hence the present disclosure addresses the tuning of the PID controllers for multivariable processes in an optimal manner, including the advantages of MPC, and provides solutions for such tuning of PID.